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Facing the Data Challenge: Institutions, Disciplines, Services and Risks
1. A centre of expertise in digital information management
www.ukoln.ac.uk
UKOLN is supported by:
Facing the Data Challenge
: Institutions, Disciplines,
Services & Risks
Dr Liz Lyon, Director, UKOLN, University of Bath, UK
Associate Director, UK Digital Curation Centre
1st
DCC Regional Roadshow, Bath November 2010
This work is licensed under a Creative Commons Licence
Attribution-ShareAlike 2.0
2. Overview
1. Facing the data challenge :
Requirements, Risks, Costs
2. Reviewing Data Support Services :
Analysis, Assessment, Priorities
3. Building Capacity & Capability :
Skills Audit
4. Developing a Strategic Plan :
Actions and Timeframe
12. Exercise 1a: Gathering requirements
• What are the researchers’ data requirements?
• What datasets exist already? Standards?
• What are their data priorities? Skills?
• Research methodologies? Plans?
• Equipment and instrumentation? Formats?
• Where are the “pain points”?
• How will you find out? Approaches to use?
• How will you use the information?
13. Exercise 1b: Motivation, benefits, risks
• What are the RDM drivers and enablers for
research staff and post-grad students?
• RDM drivers and enablers for Libraries / IT /
Computing Services / Information Services?
• RDM drivers and enablers for the institution?
• What are the barriers? What are the risks?
• How will you articulate the benefits?
• How will you find out? Approaches to use?
• How will you use the information?
14. Exercise 1c: Costs & sustainability
• What are the costs associated with RDM?
• For the researcher?
• For the institution?
• Direct / indirect costs? Fixed / variable costs?
• What cost data already exists?
• What time horizon are you considering?
• How will you find out? Approaches to use?
• How will you use the information?
15. • Survey e.g. Oxford, Parse.Insight
• Focus groups : semi-structured interviews
• Case studies departmental / disciplinary
• Joint R&D projects
• Data champions in departments
• Data Preservation readiness : AIDA tool
• Data audit / assessment : DAF tool
Requirements gathering:
Approaches and tools
16. Benefits:
Prioritisation of resources
Capacity development and planning
Efficiency savings – move data to more
cost-effective storage
Manage risks associated with data loss
Realise value through improved access
& re-use
Scale:
Departments, institutions
Dealing with Data Report : Rec 4
17. • DAF Implementation
Guide October 2009
• Collating lessons of
pilot studies
• Practical examples
of questionnaires and
interview frameworks
• DAF online tool
autumn 2010
http://www.data-audit.eu/docs/DAF_Implementation_Guide.pdf
21. Some lessons learned….
“CeRch had four false starts before finding a willing audit partner”
“Pick your moment” ….“Timing is key” (avoid exams, field trips,
Boards…)
Plan well in advance!
“Be prepared to badger senior management”
Little documentation/knowledge of what exists:“a nightmare”
Defining the scope and granularity is
crucial
Collect as much information as possible in interviews/surveys
Variable openness of staff and their data
22. Identifying risks
• Data loss (institution, research group,
individual)
• Increased costs (lack of planning, service
inefficency, data loss)
• Legal compliance (research funder, H&S,
ethics, FoI)
• Reputation (institution, unit, individual)
25. Dimension 1
Direct Indirect (costs avoided)
Dimension 2
Near-term Long-term
Dimension 3
Private Public
Benefits Taxonomy: Summary
Keeping Research Data Safe2 Report: April 2010
26. KRDS
• Which costs?
• Effect over time?
• Benefits taxonomy
• Repository models
• Case studies
• Key cost variables
• Recommendations
• User Guide,
business templates
forthcoming 2010
29. A centre of expertise in digital information management
www.ukoln.ac.uk
1. Leadership
2. Policy
3. Planning
4. Audit
6. Repositories &
Quality assurance
8. Access &
Re-use
10. Community
building
9. Training & skills
Data
Informatics
Top 10
5. Engagement
7. Sustainability
44. DMPs next steps?
• Embed DMPs in funder policies &
research lifecycles as the norm
• Code of Conduct for Research
• Assess & review DMPs (not just
the science content of proposals)
• Educate reviewers (DCC guidance
for social science in prep)
• Manage compliance of researchers
• Infrastructure to share DMPs
• Integrate in institution research
management information system
47. Data challenges?
1. Data management plans
2. Appraisal: selection criteria
3. Data retention and handover
4. Data documentation: metadata,
schema, semantics
5. Data formats: applying standards
6. Instrumentation: proprietary formats
7. Data provenance: authenticity
8. Data citation & versions: persistent IDs
9. Data validation and reproducibility
10. Data access: embargo policy
11. Data licensing
12. Data linking: text, images, software
48. Exercise 3: Skills Audit
• What skills do you have in house?
• What are your strengths? Core data
skills?
• Gaps? Do these matter?
• Can / should they be developed?
• How? Resource implications?
• Other sources of expertise?
• Key partnerships?
• Team science roles?
50. Data Access & Re-use
“Community Criteria for Interoperability”
(Scaling Up Report 2008)
• Domain data format standard: CIF
• Domain data validation standard: CheckCIF
• Metadata schema: eCrystals Application Profile
http://www.ukoln.ac.uk/projects/ebank-uk/schemas/
• Crystallography Data Commons:
TIDCC Data Model in development
• Domain identifier: International Chemical Identifier
• Citation & linking: DOI
http://dx.doi.org/10.1594/ecrystals.chem.soton.ac.uk/145
• Embargo & Rights http://ecrystals.chem.soton.ac.uk/rights.html
51. Data Licensing
• Bespoke licences
• Standard licences
• Multiple licensing
• Licence mechanisms
• Forthcoming 2010
57. Training
• Consortial
• Institutional
• Departmental
• Laboratory
• Project
• Library
• Computing Services
• Research staff / postdocs
• Postgraduate students
“excellent : probably the
best course I have been on
since starting my role as an
Informatics Liaison Officer”
Research Data Management Forum
http://www.dcc.ac.uk/data-forum/
1) Roles & Responsibilities
2) Value & Benefits
3) Sensitive Data: Ethics, Security, Trust
4) Economics of Applying & Sustaining
digital curation
58. • Online resources
• Includes training for
• Data handling
• Software
• SPSS, NVIVO
59. • Live arts
• Department of
Drama
• Researcher-
practitioner focus
62. Optimising organisational support
• Organisational structures
• Library / IT / IS / research support structure
• Where does data management fit?
• Leadership?
• Co-ordination?
• Roles : data librarian, data manager,
research support officer, data scientist, data
curator...
• New roles?
64. Exercise 4: Actions and Timeframe
• Vision and Objectives: Are they clear?
• Organisational structures: Fit for purpose?
• Library / IT / IS structure : Is it optimal?
• Roles : who is best placed to take action?
• Responsibility : for each service / activity?
• Priorities : what will you stop doing?
• Resources : Do you need to bid for
funding?
• Partnerships : Who do you need to talk to?
• Plan: What? Who? How? When?
66. Take homes
1. Understand the research data
requirements of your
campus / institutional
consumers
2. Agree research data service
delivery priorities
3. Define data roles and
responsibilities
4. Collaborate and strengthen
the data support provided
5. Be pro-active! Engage! Be
part of team science!
http://www.pnl.gov/science/images/highlights/computing/biopilotlg.jpg
